Maybe the real question is: “Is quantum computing ready for you and me?”
Because, currently, quantum computing (QC) is an immature technology in search of a problem – one that it can solve more economically than other readily available solutions.
QC hardware must scale up by a factor of 10,000 before business-relevant algorithms can be processed. This, experts believe, should happen within the next 10 years. So, is QC relevant in the meantime?
While the search for business-relevant QC algorithms continues, progress has been slow over the last two decades and has been hampered by factors both intrinsic to quantum computing (e.g., extremely alien quantum logic) and extrinsic (e.g., no Moore’s law in software engineering in general).
The promise of quantum
The future for quantum computing lies in its capacity to execute selected algorithms much faster – or involving much more data – than available classical computers can do. This comes from the fact that quantum computers do not operate on ordinary bits with just the two states ‘0’ or ‘1,’ but so-called qubits where one needs two real numbers to exactly describe the state of a single qubit. Also contrary to normal bits, a multi-qubit quantum states needs exponentially more parameters to be exactly specified.
In certain cases, “much faster” means exponentially faster; this so-called “quantum speed-up” or “quantum advantage” is only achievable for particular algorithms. It allows you to widen the problem space you can track computationally in the following dimensions:
More complex algorithms: More operations, more decision rules
- More data: larger systems, finer resolution, better accuracy
- Long-lasting algorithms: More calculation steps
- Higher precision: Higher accuracy of results.
Several (difficult to understand, even for experts) theoretical concepts as to how quantum systems behave and interact contribute to this quantum advantage. But a precise understanding where the power of quantum computing comes from remains an open research question.
QC, todays’ technology-in-search-of-a-problem, will never replace classical computers. Instead, so-called quantum processing units (QPUs) will augment classical computers like co-processors. Problem-solving using QPUs will then involve a mix of classical software with quantum algorithms.
So, why am I discussing it now? Because at Software AG, there are two ways we can support this computing model:
- Data pump & back-channel for quantum computing: Our Cumulocity IoT and webMethods integration platforms are perfectly suited to move the massive amounts of data that QC will be able to process (in the strategic future) from the IoT and enterprise IT systems domain to the QPUs. The same platforms also provide an easy back channel to convey results from quantum algorithms back to business-relevant IT systems or IoT endpoints.
- QC disintermediation – providing an access plane linking classical & quantum computing: Our webMethods integration platform will be able to enact and integrate the workflows involved in carrying out quantum algorithms, as this involves a plethora of different quantum computation-related IT systems (such as software archives or code repositories).
Furthermore, as different providers of QCaaS (quantum computing as a service) emerge with differing computational capabilities, the webMethods integration platform will be able to disintermediate between these and an organization’s classical computing facilities. Thereby enterprises will be able to unlock the value provided by many different QC vendors.
Our own usage of quantum computing
While we are currently not aware of any quantum algorithm that is directly relevant to the capabilities of our platforms and products, it remains conceivable that quantum algorithms may augment our products at a later point in time.
This includes our Cumulocity IoT platform where TrendMiner, its self-service industrial (= time series) analytics engine, might profit from suitable quantum algorithms for its analytics or predictive capabilities. ARIS Process Mining could also resort to specific quantum machine learning algorithms when dealing with extraordinary amounts of data to analyze, perhaps for seamlessly including millions of IoT endpoints.
To read the complete version of this whitepaper, click here.
And to learn more about how Software AG can help you prepare for QC – and the future – click below.